Automatic Detection of Cutaneous Lesions

Abstract

A computerized system and method for analyzing a digital photograph containing identified skin parts and analyzing and identifying cutaneous lesions. The method comprises: enhancing lesions in the identified skin parts; detecting hair patches; approximating localization of all lesions, and identifying lesions pixels.

1 . A computing system comprising:
 at least one processor; and   at least one memory communicatively coupled to the at least one processor comprising computer-readable instructions that when executed by the at least one processor cause the computing system to implement a method for analyzing a digital photograph comprising identified skin parts and analyzing cutaneous lesions, the method comprising the steps of:   (i) enhancing lesions in said identified skin parts, wherein said enhancing lesions comprises the steps of:
 a. detecting skin complexion using common/averaged value of density estimation on a dominant channel extracted from skin pixels; 
 b. boosting lesions pixels by enhancement of lesion pixels and suppression of skin pixels; and 
 c. enhancing said Dominant Channel by combining said Dominant Channel with lesions boosting mechanism; 
   (ii) detecting hair patches;   (iii) approximating localization of all lesions; and   (iv) identifying lesions pixels.   
     
     
         2 . The computing system according to  claim 1 , wherein said dominant channel is saturation, value, intensity, Red Green Blue (RGB) or any combination thereof. 
     
     
         3 . The computing system according to  claim 1 , wherein detecting hair patches comprises the steps of:
 (i) calculating one or more hair detection filters based on enhanced dominant channel (EDC);   (ii) calculating local normalized median or average on the filtered EDC;   (iii) calculating density estimation on the “value-EDC” planes or other planes;   (iv) detecting clusters;   (v) calculating how close is each cluster to hair color and skin color and assigning a hair color score to each cluster; and   (vi) assigning a patch hair probability score to each cluster based on each cluster's hair color score and savannah score.   
     
     
         4 . The computing system according to  claim 3 , wherein detecting clusters is performed using semi-supervised k-means or spectral clustering or any other clustering method. 
     
     
         5 . The computing system according to  claim 1 , wherein approximating localization of all lesions comprises the steps of:
 (i) calculating one or more edge detection filters on EDC plane, said filters varying in length and coefficients values;   (ii) calculating of local median; average; median and standard deviation; or average and standard deviation on the filtered magnitude EDC image;   (iii) combining the results of step (i) and (ii) to create an automatic threshold setting for segmentation for each and every pixel on all regions and for every filter;   (iv) combining said various pixels outcomes and filters decisions to a objects candidates map;   (v) cleaning, smoothing and unifying objects based on filters and proximity;   (vi) filling small holes and gaps;   (vii) removing candidates that are not fully shown in a skin region or in entire image;   (viii) removing candidates that are too small, too narrow or too lacy; and   (ix) cleaning, smoothing and unifying objects again based on morphological filters.   
     
     
         6 . The computing system according to  claim 5 , wherein said edge detection filters are of different shapes, sizes and structures based partly on patch hair probability scores. 
     
     
         7 . The computing system according to  claim 5 , wherein said morphological filters are operations to clean, smooth and remove small blobs and consolidate blobs. 
     
     
         8 . The computing system according to  claim 5 , wherein said morphological filters size is A*B, where A and B are a number between 1-15. 
     
     
         9 . The computing system according to  claim 1 , wherein identifying of lesion pixels comprises performing the following steps for each lesion candidate:
 (i) taking from image planes red/green/blue/value/EDC or any combination of one or more of said image planes the pixels that include the lesion candidate as well as its neighboring pixels;   (ii) performing density estimation and maximization of the inter class variation in order to get a suggested threshold for accurate segmentation;   (iii) verifying that the suggested threshold from (ii) is within a defined range;   (iv) perform thresholding, thus creating candidate objects;   (v) cleaning, smoothing and unifying objects based on morphological filters and proximity; and   (vi) fill small holes and gaps.   
     
     
         10 . The computing system according to  claim 9 , further comprising the step of removing candidates based on one or more morphological features, wherein said one or more morphological features comprise: Area, Elongation, Euler number, Eccentricity, Major Axis Length, Convex ratio, Convex area, normalized Extent, Extent, normalized Solidity, Solidity. 
     
     
         11 . (canceled) 
     
     
         12 . The computing system according to  claim 1 , wherein said digital photograph was taken according to a total body photography protocol. 
     
     
         13 . The computing system according to  claim 1 , wherein the lesions detected are of 0.5 millimeter (mm) or bigger. 
     
     
         14 . A computer system comprising:
 a processor; and   a memory communicatively coupled to the processor comprising computer-readable instructions that when executed by the processor cause the computer system to execute instructions for analyzing a digital photograph comprising identified skin parts and analyzing cutaneous lesions, the system comprising:   (i) an enhancement module adapted to enhancing via the processor lesions in said identified skin parts, wherein said enhancing lesions comprises the steps of:
 a. detecting skin complexion using common/averaged value of density estimation on a dominant channel extracted from skin pixels, wherein said dominant channel is saturation, value, intensity, Red Green Blue (RGB) or any combination thereof; 
 b. boosting lesions pixels by enhancement of lesion pixels and suppression of skin pixels; and 
 c. enhancing said Dominant Channel by combining said Dominant Channel with lesions boosting mechanism; 
   (ii) a detection module adapted for detecting via the processor hair patches;   (iii) an approximation module adapted for approximating via the processor localization of all lesions; and   (iv) an identification module adapted for identifying via the processor lesions pixels.   
     
     
         15 . (canceled) 
     
     
         16 . The computer system according to  claim 14 , wherein said detection module is further adapted for:
 (i) calculating one or more hair detection filters based on enhanced dominant channel (EDC);   (ii) calculating local normalized median or average on the filtered EDC;   (iii) calculating density estimation on the “value-EDC” planes or other planes;   (iv) detecting clusters;   (v) calculating how close is each cluster to hair color and skin color and assigning a hair color score to each cluster; and   (vi) assigning a patch hair probability score to each cluster based on each cluster's hair color score and savannah score.   
     
     
         17 . The computer system according to  claim 16 , wherein detecting clusters is performed using semi-supervised k-means or spectral clustering or any other clustering method. 
     
     
         18 . The computer system according to  claim 14 , wherein said approximation module is further adapted for:
 (i) calculating one or more edge detection filters on EDC plane, said filters varying in length and coefficients values;   (ii) calculating of local median; average; median and standard deviation; or average and standard deviation on the filtered magnitude EDC image;   (iii) combining the results of step (i) and (ii) to create an automatic threshold setting for segmentation for each and every pixel on all regions and for every filter;   (iv) combining said various pixels outcomes and filters decisions to a objects candidates map;   (v) cleaning, smoothing and unifying objects based on filters and proximity;   (vi) filling small holes and gaps;   (vii) removing candidates that are not fully shown in a skin region or in entire image;   (viii) removing candidates that are too small, too narrow or too lacy; and   (ix) cleaning, smoothing and unifying objects again based on morphological filters.   
     
     
         19 . The computer system according to  claim 18 , wherein said edge detection filters are of different shapes, sizes and structures based partly on patch hair probability scores. 
     
     
         20 . The computer system according to  claim 18 , wherein said morphological filters are operations to clean, smooth and remove small blobs and consolidate blobs. 
     
     
         21 . (canceled) 
     
     
         22 . The computer system according to  claim 14 , wherein said identification module is further adapted to perform for each lesion candidate:
 (i) taking from image planes red/green/blue/value/EDC or any combination of one or more of said image planes the pixels that include the lesion candidate as well as its neighboring pixels;   (ii) performing density estimation and maximization of the inter class variation in order to get a suggested threshold for accurate segmentation;   (iii) verifying that the suggested threshold from (ii) is within a defined range;   (iv) perform thresholding, thus creating candidate objects;   (v) cleaning, smoothing and unifying objects based on morphological filters and proximity; and   (vi) fill small holes and gaps.   
     
     
         23 . The computer system according to  claim 22 , further adapted for removing candidates based on one or more morphological features, wherein said one or more morphological features comprise: Area, Elongation, Euler number, Eccentricity, Major Axis Length, Convex ratio, Convex area, normalized Extent, Extent, normalized Solidity, Solidity. 
     
     
         24 . (canceled) 
     
     
         25 . (canceled) 
     
     
         26 . (canceled)